Landmark Graphics Corporation

Pavilion Technologies, Inc.

Landmark Graphics Corporation, a Halliburton Company

William Douglas Johnson

Laurence Reid

Eric J Hartman

Alvin Stanley Cullick

Ralph Bruce Ferguson

Richard G Ella

Andrew P Russell

Stephen William Piche

James David Keeler

Kadir Liano

Mark Gerules

Bijan Sayyar-Rodsari

Luigi Saputelli

Carl Anthony Schweiger

Celso Axelrud

Jose Antonio Rodriguez

Gerardo Mijares

Alejandro Garcia

Edward Stanley Plumer

Eric Hurley

Michael Szatny

Jordani Rebeschini

Sergio Henrique Guerra De Sousa

Andrew Russell

Richard Ella

Systems and methods for reducing run time for a reservoir simulator model using a proxy model based on a neural network.

Systems and methods for reducing reservoir simulator model run time

Systems and methods for implementing complex and disparate workflows, wherein a flexible framework (system architecture) is used for workflow automation, adaptation and integration.

Systems and methods for workflow automation, adaptation and integration

Methods, systems, and computer readable media are provided for fast updating of oil and gas field production optimization using physical and proxy simulators. A base model of a reservoir, well, or a pipeline network is established in one or more physical simulators. A decision management system is used to define uncertain parameters for matching with observed data. A proxy model is used to fit the uncertain parameters to outputs of the physical simulators, determine sensitivities of the uncertain parameters, and compute correlations between the uncertain parameters and output data from the physical simulators. Parameters for which the sensitivities are below a threshold are eliminated. The decision management system validates parameters which are output from the proxy model in the simulators. The validated parameters are used to make production decisions.

Methods, systems, and computer readable media for fast updating of oil and gas field production models with physical and proxy simulators

Methods, systems, and computer readable media are provided for real-time oil and gas field production optimization using a proxy simulator. A base model of a reservoir, well, pipeline network, or processing system is established in one or more physical simulators. A decision management system is used to define control parameters, such as valve settings, for matching with observed data. A proxy model is used to fit the control parameters to outputs of the physical simulators, determine sensitivities of the control parameters, and compute correlations between the control parameters and output data from the simulators. Control parameters for which the sensitivities are below a threshold are eliminated. The decision management system validates control parameters which are output from the proxy model in the simulators. The proxy model may be used for predicting future control settings for the control parameters.

Methods, systems, and computer-readable media for real-time oil and gas field production optimization using a proxy simulator

Data about operation of a well for extracting a product from the subterranean reservoir and at least one of a characteristic of a subterranean reservoir or operation of a processing and transport system upstream of a point of sale is received. A corrective action can be automatically initiated on at least one of the well or the processing and transport system in response to a difference between the received data and a specified operational objective. An adjustment to a model of the reservoir, the well, and the processing and transporting system can be automatically initiated in response to the received data.

Dynamic production system management

A system and method for historical database training of a support vector machine (SVM). The SVM is trained with training sets from a stream of process data. The system detects availability of new training data, and constructs a training set from the corresponding input data. Over time, many training sets are presented to the SVM. When multiple presentations are needed to effectively train the SVM, a buffer of training sets is filled and updated as new training data becomes available. Once the buffer is full, a new training set bumps the oldest training set from the buffer. The training sets are presented one or more times each time a new training set is constructed. A historical database of time-stamped data may be used to construct training sets for the SVM. The SVM may be trained retrospectively by searching the historical database and constructing training sets based on the time-stamped data.

System and method for historical database training of support vector machines

A system and method for performing modeling, prediction, optimization, and control, including an enterprise wide framework for constructing modeling, optimization, and control solutions. The framework includes a plurality of base classes that may be used to create primitive software objects. These objects may then be combined to create optimization and/or control solutions. The distributed event-driven component architecture allows much greater flexibility and power in creating, deploying, and modifying modeling, optimization and control solutions. The system also includes various techniques for performing improved modeling, optimization, and control, as well as improved scheduling and control. For example, the system may include a combination of batch and continuous processing frameworks, and a unified hybrid modeling framework which allows encapsulation and composition of different model types, such as first principles models and empirical models. The system further includes an integrated process scheduling solution referred to as process coordinator that seamlessly incorporates the capabilities of advanced control and execution into a real time event triggered optimal scheduling solution.

System and method for enterprise modeling, optimization and control

A method for modeling a steady-state network in the absence of steady-state historical data. A steady-state neural network can be tied by impressing the dynamics of the system onto the input data during the training operation by first determining the dynamics in a local region of the input space, this providing a set of dynamic training data. This dynamic training data is then utilized to train a dynamic model, gain thereof then set equal to unity such that the dynamic model is now valid over the entire input space. This is a linear model, and the historical data over the entire input space is then processed through this model prior to input to the neural network during training thereof to remove the dynamic component from the data, leaving the steady-state component for the purpose of training. This provides a valid model in the presence of historical data that has a large content of dynamic behavior. A single dynamic model is required for each output variable in a multi-input multi-output steady-state model such that for each output there is a separate dynamic model required for pre-filtering. They are combined in a single network made up of multiple individual steady-state models for each output. The dynamic model can be identified utilizing a weighting factor for the gain to force the dynamic gain of the dynamic model to the steady-state gain by weighting the difference thereof during optimization of the dynamic model. The steady-state model is optimized utilizing gain constraints during the optimization procedure such that the gain of the network is prevented from exceeding the gain constraints.

Method for steady-state identification based upon identified dynamics

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Austin, TX, United States of America

William Douglas Johnson